



import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple

import torch
from audiocraft.models import MusicGen

from transformers import (
    AutoFeatureExtractor,
    AutoTokenizer,
    EncodecModel,
    MusicgenDecoderConfig,
    MusicgenForConditionalGeneration,
    MusicgenProcessor,
    TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging


logging.set_verbosity_info()
a      :  Optional[Any]			     =			logging.get_logger(__name__)


a      :  Tuple			     =			['model.decoder.embed_positions.weights']







def  lowerCAmelCase_	(lowerCAmelCase__:		Tuple  ):





			"""simple docstring"""


			if "emb" in name:
						UpperCAmelCase_:		Dict							      =     name.replace("""emb"""		,						"""model.decoder.embed_tokens"""  )
			if "transformer" in name:
						UpperCAmelCase_:		Union[str, Any]							      =     name.replace("""transformer"""		,						"""model.decoder"""  )
			if "cross_attention" in name:
						UpperCAmelCase_:		List[Any]							      =     name.replace("""cross_attention"""		,						"""encoder_attn"""  )
			if "linear1" in name:
						UpperCAmelCase_:		Optional[Any]							      =     name.replace("""linear1"""		,						"""fc1"""  )
			if "linear2" in name:
						UpperCAmelCase_:		Tuple							      =     name.replace("""linear2"""		,						"""fc2"""  )
			if "norm1" in name:
						UpperCAmelCase_:		List[str]							      =     name.replace("""norm1"""		,						"""self_attn_layer_norm"""  )
			if "norm_cross" in name:
						UpperCAmelCase_:		Optional[int]							      =     name.replace("""norm_cross"""		,						"""encoder_attn_layer_norm"""  )
			if "norm2" in name:
						UpperCAmelCase_:		Tuple							      =     name.replace("""norm2"""		,						"""final_layer_norm"""  )
			if "out_norm" in name:
						UpperCAmelCase_:		Tuple							      =     name.replace("""out_norm"""		,						"""model.decoder.layer_norm"""  )
			if "linears" in name:
						UpperCAmelCase_:		Dict							      =     name.replace("""linears"""		,						"""lm_heads"""  )
			if "condition_provider.conditioners.description.output_proj" in name:
						UpperCAmelCase_:		int							      =     name.replace("""condition_provider.conditioners.description.output_proj"""		,						"""enc_to_dec_proj"""  )
			return name







def  lowerCAmelCase_	(lowerCAmelCase__:		OrderedDict		,						lowerCAmelCase__:		int  ):





			"""simple docstring"""


			UpperCAmelCase_:		str							      =     list(state_dict.keys()  )
			UpperCAmelCase_:		int							      =     {}
			for key in keys:
						UpperCAmelCase_:		Tuple							      =     state_dict.pop(lowerCAmelCase__  )
						UpperCAmelCase_:		Optional[Any]							      =     rename_keys(lowerCAmelCase__  )
						if "in_proj_weight" in key:
									# split fused qkv proj
									UpperCAmelCase_:		List[Any]							      =     val[:hidden_size, :]
									UpperCAmelCase_:		List[str]							      =     val[hidden_size : 2 * hidden_size, :]
									UpperCAmelCase_:		Tuple							      =     val[-hidden_size:, :]
						elif "enc_to_dec_proj" in key:
									UpperCAmelCase_:		Union[str, Any]							      =     val
						else:
									UpperCAmelCase_:		Optional[Any]							      =     val
			return state_dict, enc_dec_proj_state_dict







def  lowerCAmelCase_	(lowerCAmelCase__:		str  ):





			"""simple docstring"""


			if checkpoint == "small":
						# default config values
						UpperCAmelCase_:		List[Any]							      =     1_0_2_4
						UpperCAmelCase_:		Any							      =     2_4
						UpperCAmelCase_:		List[str]							      =     1_6
			elif checkpoint == "medium":
						UpperCAmelCase_:		Optional[int]							      =     1_5_3_6
						UpperCAmelCase_:		Union[str, Any]							      =     4_8
						UpperCAmelCase_:		Union[str, Any]							      =     2_4
			elif checkpoint == "large":
						UpperCAmelCase_:		str							      =     2_0_4_8
						UpperCAmelCase_:		Union[str, Any]							      =     4_8
						UpperCAmelCase_:		Union[str, Any]							      =     3_2
			else:
						raise ValueError(F'Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.'  )
			UpperCAmelCase_:		Union[str, Any]							      =     MusicgenDecoderConfig(
			    hidden_size=lowerCAmelCase__		,						ffn_dim=hidden_size * 4		,						num_hidden_layers=lowerCAmelCase__		,						num_attention_heads=lowerCAmelCase__		,						)
			return config







@torch.no_grad()
def  lowerCAmelCase_	(lowerCAmelCase__:		Dict		,						lowerCAmelCase__:		Optional[Any]=None		,						lowerCAmelCase__:		Union[str, Any]=None		,						lowerCAmelCase__:		int="cpu"  ):





			"""simple docstring"""


			UpperCAmelCase_:		Any							      =     MusicGen.get_pretrained(lowerCAmelCase__		,						device=lowerCAmelCase__  )
			UpperCAmelCase_:		Optional[int]							      =     decoder_config_from_checkpoint(lowerCAmelCase__  )

			UpperCAmelCase_:		Optional[int]							      =     fairseq_model.lm.state_dict()
			UpperCAmelCase_      ,							UpperCAmelCase_:		Any							      =     rename_state_dict(
			    lowerCAmelCase__		,						hidden_size=decoder_config.hidden_size  )

			UpperCAmelCase_:		List[str]							      =     TaEncoderModel.from_pretrained("""t5-base"""  )
			UpperCAmelCase_:		str							      =     EncodecModel.from_pretrained("""facebook/encodec_32khz"""  )
			UpperCAmelCase_:		List[Any]							      =     MusicgenForCausalLM(lowerCAmelCase__  ).eval()

			# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
			UpperCAmelCase_      ,							UpperCAmelCase_:		Tuple							      =     decoder.load_state_dict(lowerCAmelCase__		,						strict=lowerCAmelCase__  )

			for key in missing_keys.copy():
						if key.startswith(("""text_encoder""", """audio_encoder""")  ) or key in EXPECTED_MISSING_KEYS:
									missing_keys.remove(lowerCAmelCase__  )

			if len(lowerCAmelCase__  ) > 0:
						raise ValueError(F'Missing key(s) in state_dict: {missing_keys}'  )

			if len(lowerCAmelCase__  ) > 0:
						raise ValueError(F'Unexpected key(s) in state_dict: {unexpected_keys}'  )

			# init the composite model
			UpperCAmelCase_:		Dict							      =     MusicgenForConditionalGeneration(text_encoder=lowerCAmelCase__		,						audio_encoder=lowerCAmelCase__		,						decoder=lowerCAmelCase__  )

			# load the pre-trained enc-dec projection (from the decoder state dict)
			model.enc_to_dec_proj.load_state_dict(lowerCAmelCase__  )

			# check we can do a forward pass
			UpperCAmelCase_:		Optional[Any]							      =     torch.arange(0		,						8		,						dtype=torch.long  ).reshape(2		,						-1  )
			UpperCAmelCase_:		Tuple							      =     input_ids.reshape(2 * 4		,						-1  )

			with torch.no_grad():
						UpperCAmelCase_:		int							      =     model(input_ids=lowerCAmelCase__		,						decoder_input_ids=lowerCAmelCase__  ).logits

			if logits.shape != (8, 1, 2_0_4_8):
						raise ValueError("""Incorrect shape for logits"""  )

			# now construct the processor
			UpperCAmelCase_:		str							      =     AutoTokenizer.from_pretrained("""t5-base"""  )
			UpperCAmelCase_:		Any							      =     AutoFeatureExtractor.from_pretrained("""facebook/encodec_32khz"""		,						padding_side="""left"""  )

			UpperCAmelCase_:		Optional[Any]							      =     MusicgenProcessor(feature_extractor=lowerCAmelCase__		,						tokenizer=lowerCAmelCase__  )

			# set the appropriate bos/pad token ids
			UpperCAmelCase_:		Optional[Any]							      =     2_0_4_8
			UpperCAmelCase_:		int							      =     2_0_4_8

			# set other default generation config params
			UpperCAmelCase_:		Tuple							      =     int(3_0 * audio_encoder.config.frame_rate  )
			UpperCAmelCase_:		Any							      =     True
			UpperCAmelCase_:		Optional[int]							      =     3.0

			if pytorch_dump_folder is not None:
						Path(lowerCAmelCase__  ).mkdir(exist_ok=lowerCAmelCase__  )
						logger.info(F'Saving model {checkpoint} to {pytorch_dump_folder}'  )
						model.save_pretrained(lowerCAmelCase__  )
						processor.save_pretrained(lowerCAmelCase__  )

			if repo_id:
						logger.info(F'Pushing model {checkpoint} to {repo_id}'  )
						model.push_to_hub(lowerCAmelCase__  )
						processor.push_to_hub(lowerCAmelCase__  )


if __name__ == "__main__":
	a      :  Union[str, Any]			     =			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--checkpoint',
	    default='small',
	    type=str,
	    help='Checkpoint size of the MusicGen model you\'d like to convert. Can be one of: `[\'small\', \'medium\', \'large\']`.',
	)
	parser.add_argument(
	    '--pytorch_dump_folder',
	    required=True,
	    default=None,
	    type=str,
	    help='Path to the output PyTorch model directory.',
	)
	parser.add_argument(
	    '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
	)
	parser.add_argument(
	    '--device', default='cpu', type=str, help='Torch device to run the conversion, either cpu or cuda.'
	)

	a      :  Tuple			     =			parser.parse_args()
	convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)











def  lowerCAmelCase_	(lowerCAmelCase__:		Union[str, Any]		,						lowerCAmelCase__:		Optional[int]  ):





			"""simple docstring"""


			UpperCAmelCase_:		List[str]							      =     0
			UpperCAmelCase_:		Tuple							      =     len(lowerCAmelCase__  ) - 1

			while left <= right:
						# avoid divided by 0 during interpolation
						if sorted_collection[left] == sorted_collection[right]:
									if sorted_collection[left] == item:
												return left
									else:
												return None

						UpperCAmelCase_:		Dict							      =     left + ((item - sorted_collection[left]) * (right - left)) // (
						    sorted_collection[right] - sorted_collection[left]
						)

						# out of range check
						if point < 0 or point >= len(lowerCAmelCase__  ):
									return None

						UpperCAmelCase_:		str							      =     sorted_collection[point]
						if current_item == item:
									return point
						else:
									if point < left:
												UpperCAmelCase_:		int							      =     left
												UpperCAmelCase_:		str							      =     point
									elif point > right:
												UpperCAmelCase_:		List[str]							      =     right
												UpperCAmelCase_:		Optional[int]							      =     point
									else:
												if item < current_item:
															UpperCAmelCase_:		Optional[Any]							      =     point - 1
												else:
															UpperCAmelCase_:		Any							      =     point + 1
			return None







def  lowerCAmelCase_	(lowerCAmelCase__:		Dict		,						lowerCAmelCase__:		List[Any]		,						lowerCAmelCase__:		Any		,						lowerCAmelCase__:		Any  ):





			"""simple docstring"""


			if sorted_collection[left] == sorted_collection[right]:
						if sorted_collection[left] == item:
									return left
						else:
									return None

			UpperCAmelCase_:		Union[str, Any]							      =     left + ((item - sorted_collection[left]) * (right - left)) // (
			    sorted_collection[right] - sorted_collection[left]
			)

			# out of range check
			if point < 0 or point >= len(lowerCAmelCase__  ):
						return None

			if sorted_collection[point] == item:
						return point
			elif point < left:
						return interpolation_search_by_recursion(lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__  )
			elif point > right:
						return interpolation_search_by_recursion(lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__  )
			else:
						if sorted_collection[point] > item:
									return interpolation_search_by_recursion(
									    lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__		,						point - 1  )
						else:
									return interpolation_search_by_recursion(
									    lowerCAmelCase__		,						lowerCAmelCase__		,						point + 1		,						lowerCAmelCase__  )







def  lowerCAmelCase_	(lowerCAmelCase__:		Dict  ):





			"""simple docstring"""


			if collection != sorted(lowerCAmelCase__  ):
						raise ValueError("""Collection must be ascending sorted"""  )
			return True


if __name__ == "__main__":
	import sys
	a      :  Optional[Any]			     =			0
	if debug == 1:
		a      :  int			     =			[10, 30, 40, 45, 50, 66, 77, 93]
		try:
			__assert_sorted(collection)
		except ValueError:
			sys.exit('Sequence must be ascending sorted to apply interpolation search')
		a      :  int			     =			67

	a      :  Any			     =			interpolation_search(collection, target)
	if result is not None:
		print(F'''{target} found at positions: {result}''')
	else:
		print('Not found')






